dc.contributor.author | Aerts, Hugo J. W. L. | en_US |
dc.contributor.author | Velazquez, Emmanuel Rios | en_US |
dc.contributor.author | Leijenaar, Ralph T. H. | en_US |
dc.contributor.author | Parmar, Chintan | en_US |
dc.contributor.author | Grossmann, Patrick | en_US |
dc.contributor.author | Cavalho, Sara | en_US |
dc.contributor.author | Bussink, Johan | en_US |
dc.contributor.author | Monshouwer, René | en_US |
dc.contributor.author | Haibe-Kains, Benjamin | en_US |
dc.contributor.author | Rietveld, Derek | en_US |
dc.contributor.author | Hoebers, Frank | en_US |
dc.contributor.author | Rietbergen, Michelle M. | en_US |
dc.contributor.author | Leemans, C. René | en_US |
dc.contributor.author | Dekker, Andre | en_US |
dc.contributor.author | Quackenbush, John | en_US |
dc.contributor.author | Gillies, Robert J. | en_US |
dc.contributor.author | Lambin, Philippe | en_US |
dc.date.accessioned | 2014-07-07T17:03:36Z | |
dc.date.issued | 2014 | en_US |
dc.identifier.citation | Aerts, H. J. W. L., E. R. Velazquez, R. T. H. Leijenaar, C. Parmar, P. Grossmann, S. Cavalho, J. Bussink, et al. 2014. “Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.” Nature Communications 5 (1): 4006. doi:10.1038/ncomms5006. http://dx.doi.org/10.1038/ncomms5006. | en |
dc.identifier.issn | 2041-1723 | en |
dc.identifier.uri | http://nrs.harvard.edu/urn-3:HUL.InstRepos:12406714 | |
dc.description.abstract | Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. | en |
dc.language.iso | en_US | en |
dc.publisher | Nature Pub. Group | en |
dc.relation.isversionof | doi:10.1038/ncomms5006 | en |
dc.relation.hasversion | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4059926/pdf/ | en |
dash.license | LAA | en_US |
dc.title | Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach | en |
dc.type | Journal Article | en_US |
dc.description.version | Version of Record | en |
dc.relation.journal | Nature Communications | en |
dash.depositing.author | Aerts, Hugo J. W. L. | en_US |
dc.date.available | 2014-07-07T17:03:36Z | |
dc.identifier.doi | 10.1038/ncomms5006 | * |
dash.authorsordered | false | |
dash.contributor.affiliated | Grossmann, Patrick | |
dash.contributor.affiliated | Aerts, Hugo | |